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Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies
Abstract
Background: Most of our social interactions involve perception of emotional information from the faces of other people. Furthermore, such emotional processes are thought to be aberrant in a range of clinical disorders, including psychosis and depression. However, the exact neurofunctional maps underlying emotional facial processing are not well defined. Methods: Two independent researchers conducted separate comprehensive PubMed (1990 to May 2008) searches to find all functional magnetic resonance imaging (fMRI) studies using a variant of the emotional faces paradigm in healthy participants. The search terms were: “fMRI AND happy faces,” “fMRI AND sad faces,” “fMRI AND fearful faces,” “fMRI AND angry faces,” “fMRI AND disgusted faces” and “fMRI AND neutral faces.” We extracted spatial coordinates and inserted them in an electronic database. We performed activation likelihood estimation analysis for voxel-based meta-analyses. Results: Of the originally identified studies, 105 met our inclusion criteria. The overall database consisted of 1785 brain coordinates that yielded an overall sample of 1600 healthy participants. Quantitative voxel-based meta-analysis of brain activation provided neurofunctional maps for 1) main effect of human faces; 2) main effect of emotional valence; and 3) modulatory effect of age, sex, explicit versus implicit processing and magnetic field strength. Processing of emotional faces was associated with increased activation in a number of visual, limbic, temporoparietal and prefrontal areas; the putamen; and the cerebellum. Happy, fearful and sad faces specifically activated the amygdala, whereas angry or disgusted faces had no effect on this brain region. Furthermore, amygdala sensitivity was greater for fearful than for happy or sad faces. Insular activation was selectively reported during processing of disgusted and angry faces. However, insular sensitivity was greater for disgusted than for angry faces. Conversely, neural response in the visual cortex and cerebellum was observable across all emotional conditions. Limitations: Although the activation likelihood estimation approach is currently one of the most powerful and reliable meta-analytical methods in neuroimaging research, it is insensitive to effect sizes. Conclusion: Our study has detailed neurofunctional maps to use as normative references in future fMRI studies of emotional facial processing in psychiatric populations. We found selective differences between neural networks underlying the basic emotions in limbic and insular brain regions.
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Cited by
Sorted by citations
The brain basis of emotion: a meta-analytic review
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11000446 · Cited by 2,320
“In fact, regions of visual cortex were some of the most frequent to appear in our meta-analytic findings (replicating several recent meta-analyses; e.g., Fusar-Poli et al. 2009; Kober et al. 2008; Vytal & Hamann 2010).”
BackgroundCognitive Reappraisal of Emotion: A Meta-Analysis of Human Neuroimaging Studies
Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., Weber, J., & Ochsner, K. N. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht154 · Cited by 1,896
Rethinking the Emotional Brain
LeDoux, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.004 · Cited by 1,594
“…for much research on the neural basis of emotional functions in the human brain—a recent review uncovered 551 studies between 1990 and 2008 that used Ekman’s basic emotions faces or variants of these to study functional activity related to emotion in the human brain (see Fusar-Poli et al., 2009).”
Millan, M. J., Agid, Y., Brüne, M., Bullmore, E. T., Carter, C. S., Clayton, N. S., Connor, R., Davis, S., Deakin, B., DeRubeis, R. J., et al. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3628 · Cited by 1,292
Social cognition in schizophrenia
Green, M. F., Horan, W. P., & Lee, J. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn4005 · Cited by 1,159
Metadata
- Authors
- Fusar-Poli, P., Placentino, A., Carletti, F., Landi, P., Allen, P., Surguladze, S., Benedetti, F., Abbamonte, M., Gasparotti, R., Barale, F., Perez, J., McGuire, P., & Politi, P.
- Affiliations
- Section of Neuroimaging, Department of Psychological Medicine, Institute of Psychiatry, King’s College London, United Kingdom
- Biological Psychiatry Unit and Dual Diagnosis Ward IRCCS, Centro San Giovanni di Dio, Fatebenefratelli, Brescia
- Section of Psychiatry, Department of Health Sciences, University of Pavia
- Section of Affective Neuroscience, Department of Psychological Medicine, Institute of Psychiatry, King’s College London, United Kingdom
- Department of Neuropsychiatric Sciences, Scientific Institute and University Vita-Salute San Raffaele, Milan, Italy
- Department of Diagnostic Imaging, Neuroradiology Unit, University of Brescia, Italy
- Venue
- Journal of Psychiatry and Neuroscience
- Publisher
- Canadian Science Publishing
- Published
- 2009-11
- Volume
- 34
- Issue
- 6
- Pages
- 418-432
- ISSN
- 1180-4882
- 1488-2434
- Cited by
- 359
- References
- 30
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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